面向健康体检数据的多目标Top-k频繁模式挖掘方法

A multi-objective Top-k frequent pattern mining approach oriented for health examination data

为解决现有的模式挖掘方法没有充分利用体检数据中检查项的异常程度与特定疾病之间相关性的问题,提出一种面向健康体检数据的多目标Top-k频繁模式挖掘方法.首先,针对体检数据的特点,提出异常度和覆盖率两个指标,在此基础上,将Top-k频繁模式挖掘建模为一个多目标优化问题;其次,针对该问题,提出一种基于偏好的种群初始化策略和一个面向模式和项的双层更新策略,并基于此设计一种高效的进化多目标优化算法进行求解.实验结果表明,所提出方法所获得的Top-k个模式不仅能够有效地反映其与特定疾病之间的关联性,而且能够提供多样化的模式,为健康管理提供重要的参考依据.

In order to solve the problem that the existing pattern mining methods do not make full use of the correlation between the abnormality of check items in the health examination data and specific diseases,this paper proposes a multi-objective Top-k frequent pattern mining approach oriented for health examination data.First,according to the characteristics of health examination data,two indicators of abnormality and coverage are proposed,and with these metrics,the Top-k frequent pattern mining is modeled as a multi-objective optimization problem.Then,an efficient evolutionary multi-objective optimization algorithm is designed to solve the problem,in which a preference-based population initialization strategy and a two-layer update strategy oriented to patterns and items are respectively proposed.The experimental results show that the achieved Top-k frequent patterns not only effectively reflect the correlation with the specific diseases,but also provide a variety of patterns,which gives an important reference for health management.